I'm having a little trouble graphing coefplot. My regression is using reghdfe command and I'm simply trying to replicate a code from a paper - therefore I'm pretty sure there is nothing wrong with the basic coding. But, it's me who is coding it the wrong way when replicating or maybe I'm missing something very silly.

Code:
global ICONTROL male ismarried age age2 black asian hispanic lths hsdegree somecollege
    
    
    global SDUMMY     i.statefip i.year
    
    
     **low-education
    qui reghdfe ln_weekearn law $ICONTROL $SDUMMY [aw=earnwt] if inrange(age,18,65) & inlist(race,200) & inrange(educ,010,071) & !inlist(educ,000,999) ///
            , absorb(i.statefip) vce(cluster statefip)
    eststo law_ls_black
    
    **high-education
    
    qui reghdfe ln_weekearn law $ICONTROL $SDUMMY [aw=earnwt] if inrange(age,18,65) & inlist(race,200) & inrange(educ,072,125) & !inlist(educ,000,999) ///
            , absorb(i.statefip) vce(cluster statefip)
    eststo law_hs_black
    
    coefplot(law_ls_black,  label("Low-education")  connect(direct)  lcolor(red) lw(medthick) msymbol(square_hollow)  msize(medlarge) mfcolor(red) mlcolor(red) mlw(medthick)) ///
                (law_hs_black,  label("High-education") connect(direct) lp(dash) lcolor(dknavy) lw(medthick) msymbol(triangle_hollow) msize(medlarge) mfcolor(dknavy) mlcolor(dknavy) mlw(medthick) ) ///                    
                , vertical levels(95) pstyle(matrix) noci ///
                connect(direct) lcolor(dknavy) lw(medthick) msize(large) mcolor(gray) ytitle("LAW  Earnings Gap", color(gs4)) ///
                ylabel(-4(0.5)3,labsize(medsmall) labcolor(gs4)) graphregion(color(white)) ///
                xline(1, lcolor(myred)) yline(0,lcolor(gs7) lw(medthin)) xlabel(1 "2017"  2 "2018" 3 "2019" 4 "2020" 5 "2021",labsize(medsmall) labcolor(gs4)) ///
                legend(order(1 "Low-education" 2 "High-education") ring(0) position(2) bmargin(large) color(gs1) c(1) region(col(white)))
This code mentioned above giving me this graph - which is not even close to what I'm expecting. My dataset is covering from years 2017-2021. So for every year that regression is supposed to get me one point in the y axis. So, there should be five points for each regression - totaling 10 points in the y axis - whereas I have nearly more than 100 on the y -axis. Besides, the x-axis labeling is quite ugly too.

Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input float(male ismarried wasmarried) byte age float(age2 black asian hispanic lths hsdegree somecollege) byte statefip int year float law int(educ race) float ln_weekearn double earnwt
1 1 0 41 1681 1 0 0 0 1 0 1 2017 0  73 200         .          0
0 1 0 62 3844 0 0 0 0 0 0 6 2017 0 111 100         .          0
1 1 0 54 2916 0 0 0 0 1 0 6 2017 0  73 100         .          0
0 1 0 49 2401 0 0 0 0 0 1 6 2017 0  92 100         .          0
1 1 0 47 2209 0 0 0 0 0 1 6 2017 0  81 100         .          0
1 0 1 80 6400 0 0 0 0 1 0 9 2017 1  73 100         .          0
1 1 0 49 2401 0 0 0 0 1 0 9 2017 1  73 100         .          0
0 0 0 46 2116 0 0 0 0 0 0 2 2017 1 124 100         .          0
0 0 0 25  625 0 0 0 0 0 0 2 2017 1 111 100         .          0
1 1 0 52 2704 0 0 0 0 0 0 8 2017 1 123 100         .          0
1 1 0 40 1600 1 0 0 0 0 1 8 2017 1  91 200  4.255071  7546.6905
1 1 0 32 1024 0 0 0 0 0 1 8 2017 1  81 100         .          0
0 0 0 27  729 1 0 0 0 0 0 8 2017 1 123 200         .          0
1 0 0 25  625 0 0 0 0 1 0 8 2017 0  73 100  4.312938  9539.8702
0 1 0 57 3249 0 0 0 0 1 0 1 2018 0  73 100         .          0
1 1 0 27  729 0 0 1 0 1 0 1 2018 0  73 100  4.484259 13248.5771
0 1 0 78 6084 0 0 0 0 1 0 6 2018 0  73 100         .          0
1 0 0 18  324 0 0 0 0 1 0 6 2018 0  73 100         .          0
0 0 0 51 2601 1 0 0 0 1 0 6 2018 1  73 200         .          0
0 1 0 75 5625 0 0 0 0 1 0 9 2018 1  73 100         .          0
1 1 0 40 1600 0 0 0 0 0 0 9 2018 1 111 100         .          0
0 1 0 65 4225 0 0 0 0 0 1 9 2018 0  81 100         .          0
1 0 1 70 4900 0 0 0 0 0 1 2 2018 1  81 100         .          0
0 0 1 33 1089 1 0 0 0 0 1 2 2018 1 81 200         .          0
1 1 0 32 1024 1 0 0 0 0 1 2 2018 1  81 200         .          0
0 0 1 37 1369 0 0 0 1 0 0 1 2019 1  71 100         .          0
1 1 0 33 1089 0 0 0 1 0 0 1 2019 0  60 100         .          0
0 0 0 50 2500 0 0 0 0 1 0 6 2019 1  73 100         .  6568.2541
1 1 0 72 5184 1 0 0 0 1 0 6 2019 1 73 200         .          0
0 1 0 67 4489 0 0 0 1 0 0 6 2019 1  60 100         .  6376.2191
0 0 1 85 7225 0 0 0 0 1 0 9 2019 1  73 100         .          0
0 0 1 60 3600 0 0 0 0 0 1 9 2019 0  81 100         .          0
0 1 0 40 1600 1 0 0 0 0 1 2 2019 0  81 200  4.433895  8396.4731
1 1 0 27  729 0 0 0 1 0 0 8 2019 0  60 100  4.466688  9539.8702
0 0 0 20  400 0 0 0 0 0 1 1 2020 0  92 100 4.5660005  7723.6274
0 0 0 54 2916 0 0 0 0 0 0 1 2020 0 111 100         .          0
0 1 0 63 3969 0 0 0 0 0 0 6 2020 1 123 100         .          0
1 0 0 29  841 1 0 0 0 1 0 6 2020 1  73 200         .          0
1 0 0 36 1296 1 0 0 0 0 0 6 2020 1 111 200  4.645167  9105.9765
1 0 0 58 3364 0 0 1 0 1 0 9 2020 0  73 100         .          0
1 1 0 49 2401 0 0 0 0 0 0 9 2020 1 125 100         .          0
0 1 0 42 1764 0 0 0 0 0 0 9 2020 0 111 100         .          0
0 0 0 39 1521 0 0 0 0 1 0 2 2020 0  73 100         .          0
1 1 0 29  841 0 0 0 0 0 0 1 2021 0 123 100         .          0
0 1 0 73 5329 0 0 0 0 1 0 1 2021 0  73 100         .  6956.9687
1 1 0 64 4096 1 0 0 0 0 1 6 2021 0  81 200         .          0
0 1 0 85 7225 0 0 0 0 0 0 6 2021 1 111 100         .          0
1 1 0 46 2116 0 0 0 1 0 0 9 2021 1  40 100         .          0
1 1 0 63 3969 0 0 0 0 1 0 9 2021 1 73 100         .          0
1 1 0 44 1936 0 0 0 0 0 1 2 2021 1  92 100         .          0
1 1 0 74 5476 0 0 0 1 0 0 2 2021 1  40 100         .          0
0 0 1 77 5929 0 0 0 0 0 1 8 2021 0  91 100         .  6382.4968